Fast and accurate prediction of protein side-chain conformations

Summary: We developed a fast and accurate side-chain modeling program [Optimized Side Chain Atomic eneRgy (OSCAR)-star] based on orientation-dependent energy functions and a rigid rotamer model. The average computing time was 18 s per protein for 218 test proteins with higher prediction accuracy (1.1% increase for χ1 and 0.8% increase for χ1+2) than the best performing program developed by other groups. We show that the energy functions, which were calibrated to tolerate the discrete errors of rigid rotamers, are appropriate for protein loop selection, especially for decoys without extensive structural refinement

More data means less inference: A pseudo-max approach to structured learning

The problem of learning to predict structured labels is of key importance in many applications. However, for general graph structure both learning and inference in this setting are intractable. Here we show that it is possible to circumvent this difficulty when the input distribution is rich enough via a method similar in spirit to pseudo-likelihood. We show how our new method achieves consistency, and illustrate empirically that it indeed performs as well as exact methods when sufficiently large training sets are used.United States-Israel Binational Science Foundation (Grant 2008303)Google (Firm) (Research Grant)Google (Firm) (PhD Fellowship

A computational framework to empower probabilistic protein design

Motivation: The task of engineering a protein to perform a target biological function is known as protein design. A commonly used paradigm casts this functional design problem as a structural one, assuming a fixed backbone. In probabilistic protein design, positional amino acid probabilities are used to create a random library of sequences to be simultaneously screened for biological activity. Clearly, certain choices of probability distributions will be more successful in yielding functional sequences. However, since the number of sequences is exponential in protein length, computational optimization of the distribution is difficult

Blending Learning and Inference in Structured Prediction

In this paper we derive an efficient algorithm to learn the parameters of structured predictors in general graphical models. This algorithm blends the learning and inference tasks, which results in a significant speedup over traditional approaches, such as conditional random fields and structured support vector machines. For this purpose we utilize the structures of the predictors to describe a low dimensional structured prediction task which encourages local consistencies within the different structures while learning the parameters of the model. Convexity of the learning task provides the means to enforce the consistencies between the different parts. The inference-learning blending algorithm that we propose is guaranteed to converge to the optimum of the low dimensional primal and dual programs. Unlike many of the existing approaches, the inference-learning blending allows us to learn efficiently high-order graphical models, over regions of any size, and very large number of parameters. We demonstrate the effectiveness of our approach, while presenting state-of-the-art results in stereo estimation, semantic segmentation, shape reconstruction, and indoor scene understanding

Blending Learning and Inference in Conditional Random Fields

Conditional random fields maximize the log-likelihood of training labels given the training data, e.g., objects given images. In many cases the training labels are structures that consist of a set of variables and the computational complexity for estimating their likelihood is exponential in the number of the variables. Learning algorithms relax this computational burden using approximate inference that is nested as a sub-procedure. In this paper we describe the objective function for nested learning and inference in conditional random fields. The devised objective maximizes the log-beliefs -probability distributions over subsets of training variables that agree on their marginal probabilities. This objective is concave and consists of two types of variables that are related to the learning and inference tasks respectively. Importantly, we afterwards show how to blend the learning and inference procedure and effectively get to the identical optimum much faster. The proposed algorithm currently achieves the state-of-the-art in various computer vision applications

Residual-guided look-ahead in AND/OR search for graphical models

We introduce the concept of local bucket error for the mini-bucket heuristics and show how it can be used to improve the power of AND/OR search for combinatorial optimization tasks in graphical models (e.g. MAP/MPE or weighted CSPs). The local bucket error illuminates how the heuristic errors are distributed in the search space, guided by the mini-bucket heuristic. We present and analyze methods for compiling the local bucket-errors (exactly and approximately) and show that they can be used to yield an effective tool for balancing look-ahead overhead during search. This can be especially instrumental when memory is restricted, accommodating the generation of only weak compiled heuristics. We illustrate the impact of the proposed schemes in an extensive empirical evaluation for both finding exact solutions and anytime suboptimal solutions.Peer ReviewedPostprint (published version

Self-Assembling Peptide Nanomaterials: Molecular Dynamics Studies, Computational Designs And Crystal Structure Characterizations

Peptides present complicated three-dimensional folds encoded in primary amino acid sequences of no more than 50 residues, providing cost-effective routes to the development of self-assembling nanomaterials.� The complexity and subtlety of the molecular interactions in such systems make it interesting to study and to understand the fundamental principles that determine the self-assembly of nanostructures and morphologies in solution. Such principles can then be applied to design novel self-assembling nanomaterials of precisely defined local structures and to controllably engineer new advanced functions into the materials. We first report the rational engineering of complementary hydrophobic interactions to control β-fibril type peptide self-assemblies that form hydrogel networks. Complementary to the experimental observations of the two distinct branching morphologies present in the two β-fibril systems that share a similar sequence pattern, we investigated on network branching, hydrogel properties by molecular dynamics simulations to provide a molecular picture of the assemblies. Next, we present the theory-guided computational design of novel peptides that adopt predetermined local nanostructures and symmetries upon solution assembly. Using such an approach, we discovered a non-natural, single peptide tetra-helical motif that can be used as a common building block for distinct predefined material nanostructures. The crystal structure of one designed peptide assembly demonstrates the atomistic match of the motif structure to the prediction, as well as provides fundamental feedback to the methods used to design and evaluate the computationally designed peptide candidates. This study could potentially improve the success rate of future designs of peptide-based self-assembling nanomaterials